Learning Semantic Graphics Using Convolutional Encoder-Decoder Network for Autonomous Weeding in Paddy
Learning Semantic Graphics Using Convolutional Encoder-Decoder Network for Autonomous Weeding in Paddy
复制标题
DOI：
10.3389/fpls.2019.01404
复制
发表时间：
2019-10-31
影响因子：
5.6
通讯作者：
Kim, Hyongsuk
中科院分区：
文献类型：
--
作者：
Adhikari, Shyam Prasad;Yang, Heechan;Kim, Hyongsuk
农场中的杂草是侵略性种植者，它们与农作物争夺营养和其他资源并减少产量。越来越多地使用化学品来控制它们，无意中对人类健康和环境造成了影响。在这项工作中，提出了一种新颖的神经网络训练方法，结合了用于数据注释的语义图形和先进的编码器-解码器网络，用于（a）自动作物线检测和（b）稻田中的杂草（野生小米）检测。检测到的作物线充当自主除草机器人进行行间除草的引导线，而杂草的检测则可以实现自主行内除草。所提出的数据注释方法，语义图形，是直观的，并且可以以最少的劳动轻松地注释所需的目标。此外，所提出的“扩展跳过网络”是一种改进的深度卷积编码器-解码器神经网络，用于有效学习语义图形。对所提出方法的定量评估表明，在稻田线检测和野生小米检测任务上，平均交集比并集（mIoU）比基线网络分别增加了 6.29% 和 6.14%。与野生小米检测任务中流行的基于边界框的目标检测方法相比，该方法还使 mIoU 提高了 3.56%，召回率显着提高。
Weeds in agricultural farms are aggressive growers which compete for nutrition and other resources with the crop and reduce production. The increasing use of chemicals to control them has inadvertent consequences to the human health and the environment. In this work, a novel neural network training method combining semantic graphics for data annotation and an advanced encoder-decoder network for (a) automatic crop line detection and (b) weed (wild millet) detection in paddy fields is proposed. The detected crop lines act as a guiding line for an autonomous weeding robot for inter-row weeding, whereas the detection of weeds enables autonomous intra-row weeding. The proposed data annotation method, semantic graphics, is intuitive, and the desired targets can be annotated easily with minimal labor. Also, the proposed "extended skip network" is an improved deep convolutional encoder-decoder neural network for efficient learning of semantic graphics. Quantitative evaluations of the proposed method demonstrated an increment of 6.29% and 6.14% in mean intersection over union (mIoU), over the baseline network on the task of paddy line detection and wild millet detection, respectively. The proposed method also leads to a 3.56% increment in mIoU and a significantly higher recall compared to a popular bounding box-based object detection approach on the task of wild-millet detection.